The paper proposes an independence‑graded audit protocol for agentic AI systems, arguing that independence should be evaluated along three orthogonal axes: principal independence, substrate independence, and evidence independence. It introduces a seven‑step protocol based on the beta‑factor model from reliability engineering, demonstrates its application through a structural detectability analysis and a Monte Carlo study, and maps the framework to relevant regulatory standards such as the EU AI Act, ISO/IEC 42006, and UK public‑sector guidance.
By Mohamed Chahine Ghanem
arXiv:2608.22512v1 Announce Type: new
Abstract: Autonomous multi-agent systems nowadays act in finance, software supply chains, and security operations. Already, the first largely AI-orchestrated int...
By Christos Sardianos, Iliana Pla, Vasilis Efthymiou, Iraklis Varlamis, Thomas Lagkas, Panagiotis Sarigiannidis, Georgios Th. Papadopoulos
The paper "Auditable Claims about AI Agents" argues that for a claim about an AI agent to be verifiable, it must explicitly state the agent’s policy, scope, the records that would confirm the claim, and who is responsible for those records. It extends the Auditable Agents framework by adding conditions of independent record coverage, action‑specific authorization, and completeness beyond integrity, and demonstrates the approach with a claim‑check table applied to six common claims. The authors provide a worked example and practical guidance for operators, buyers, auditors, and standard setters.
By Yue Zhao, Jiate Li, Li Li, Yi Nian, Jinbo Liu, Xiaolin Zhou, Xiyang Hu
AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare public failures with agent-security evaluations. We...
arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.
By Albus W. Ng, Yi Han, Jusheng Zhang, Wenhao Wang
arXiv:2606. 02282v1 Announce Type: new Abstract: Orchestrating Large Language Models into Multi-Agent Systems (LLM-MAS) has unlocked remarkable reasoning capabilities, yet emergent failures and hallucinations that resist characterisation block their deployment in safety-critical domains -- a gap made legally untenable by emerging AI regulation.
By I\~naki Dellibarda Varela, R. Sendra-Arranz, Pablo Romero-Sorozabal, J. M. Valverde-Garc\'ia, Annemarie F. Laudanski, \'Alvaro Guti\'errez, Eduardo Rocon, Manuel Cebrian
The paper "When Agents Act Unwatched: The Reduced‑Supervision Paradox in Agentic AI" discusses how the promise that AI systems will continue acting after users stop watching creates an accountability inversion. It argues that as stepwise supervision recedes, verification shifts into the runtime infrastructure—authority, records, interrupts, outcome checks, and repair—forming what the authors call the reduced‑supervision paradox. A 63‑artifact audit across research papers and engineering sources shows that agents’ action surfaces are more visible than the mechanisms needed to hold them accountable, with tool mediation and monitoring traces appearing in 40 and 37 artifacts, while checkpoint placement, validator independence, recovery, and contestability are rarely visible.
"whyItMatters":"The study highlights that observable action paths can replace accountability when verification is moved onto users after meaningful intervention is no longer possible."
By Hanjing Shi, Dominic DiFranzo
The paper reports a failure study of a production agentic software‑delivery platform, analyzing 147 incidents across 81 runs. It shows that the standard reliability primitives—retry, timeout, and error‑rate circuit breaking—fail in practice, leading to costly loops, false trips, and blocked work. The authors identify two cross‑cutting causes—identity adequacy and evidence adequacy—and propose seven new reliability primitives that enforce reliability at the delegation level.
By Mazhar Shaikh, Anurag Rajkumar Bombarde, Harshal Pathak
arXiv:2609. 11030v1 Announce Type: new Abstract: AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare public failures with agent-security evaluations.
By Divyanshu Kumar, Rohith HN, Nitin Aravind Birur, Sahil Agarwal, Prashanth Harshangi
arXiv:2604. 05485v2 Announce Type: replace Abstract: LLM agents call tools, query databases, delegate tasks, and trigger external side effects.
By Yi Nian, Aojie Yuan, Haiyue Zhang, Jiate Li, Li Li, Xiyang Hu, Hua Wei, Xiongye Xiao, Chaowei Xiao, Yue Zhao
arXiv:2606. 14589v1 Announce Type: cross Abstract: LLM agent systems increasingly run as long-lived autonomous runtimes: scheduling jobs, calling tools, maintaining memory, and pushing results to humans.
By Wei Wu
arXiv:2607. 02586v1 Announce Type: new Abstract: Governance frameworks ask AI providers and auditors for documented evaluation evidence, and perturbation-based construct-validity audits are a common form of that evidence.
By Yanhang Li, Zhichao Fan, Zexin Zhuang